An Internet search engine uses particular programs to collect information from the Internet according to certain strategies, organizes and processes the information, and provides retrieval services to users. The search engine retrieves relevant information based on keywords entered by the users, and presents the retrieved relevant information to the users as search results.
An important goal of developing the search engine technology is to minimize the number of human-machine operations of a user, and present the search results meeting the user's search intention to the user as far as possible. To achieve this goal, the search engine technology is constantly improving and developing.
Currently, there is a technology of presenting recommended query terms by the search engine, and main processes of this technology are as follows: while the user is entering a query term into a search box, the search engine finds out candidate query terms by text index based on the query term entered by the user (e.g., when the entered query term is “abc”, the candidate query terms can be those hit “abc”), and filters the candidate query terms according to statistical data such as query times and/or click rate to obtain the query terms to be finally recommended to the user (also referred to as recommended query terms in the industry), and displays the recommended query terms to the user in real time to help the user filtering out the query term of interest, so that the time taken to enter the final query term can be saved for the user and search efficiency can be improved. FIG. 1 is a diagram schematically illustrating an interface of a search engine calculating and displaying recommended query terms automatically while a user is entering a query term in accordance with the prior art. As shown in FIG. 1, upon the user enters a query term “Faye Wong” 101 in the search box, the search engine performs a process of calculating recommendations immediately to get recommended query terms and displays a list 102 containing the recommended query terms. If the user is interested in a certain one of the recommended query terms, she/he may click on the certain recommended query term to complete a web search action.
Although the above method may save the time of the user for entering the query term, the web search results cannot meet the user's vertical search requirements well. The so-called vertical search refers to a specialized search technology which focuses on a specific industry, which is a subdivision and extension of the search engine. The vertical search integrates information of a specific type in a web database, extracts needed data by fields in a directed manner, and processes the extracted data so as to return it to the user in a certain form. The vertical search is a new search engine technology and is introduced in view of the huge information amount, inaccurate search results and insufficient search depth, etc., of the conventional search engine. The vertical search provides valuable information and related services by focusing on a specific field, a specific group of people or a specific requirement. The vertical search is characterized as “specialized, accurate and deep” and can reflect characteristics of a certain industry. The vertical search is more focused, more specific and deeper compared with a general search engine which provides disorderly mass information.
For example, a web search engine among the current search engines employ a universal, comprehensive search technology, and thus its search results obtained based on a query term may contain various sub-divisional information types of results, for example a search result page may contain search results of various types, such as video, image, news, music, etc. The vertical search, however, needs to distinguish among different types of information, and one type of vertical search engine searches only one type of contents. For example, a video vertical search engine is designed to search for results of video type; while a news vertical search engine is designed to search for results of news type. Currently most search engines are equipped with different vertical search engines (the vertical search engine is also known as the vertical search channel in the industry), each of which corresponds to a different vertical search type. FIG. 2 is a schematic diagram illustrating an interface of a home page of an existing search engine, including not only a web search engine 201 (i.e. a general search engine), but also vertical search engines such as an image search engine 202, a video search engine 203, a music search engine 204, a Q&A search engine 205 (i.e., the “Question” in FIG. 2), a news search engine 206, etc.
Although the prior art as shown in FIG. 1 may save the time for entering a query term by the user, it cannot meet the user's vertical search requirements well. For example, if the user clicks on the recommended query term “Faye Wong Legend” 103 or “Faye Wong Concert in Xi'an” 104 as shown in FIG. 1, corresponding web search results will be displayed directly. However, actual search intention of a certain recommended query term mostly corresponds to a certain type of vertical search. For example, the actual search intention of “Faye Wong Legend” 103 mostly corresponds to music contents, while the actual search intention of “Faye Wong Concert in Xi'an” 104 mostly corresponds to video contents. The prior art as shown in FIG. 1 cannot separately and intuitively list the highly relevant vertical search types, so if the user intends to search for contents of a certain vertical search type, she/he needs to click a second time on a link of the associated vertical search channel to find the contents of the certain vertical search type, such as contents of music or video type.
Therefore, the search engine of the prior art is inefficient in terms of vertical search, and is inconvenient for the user to find the vertical search results that are highly relevant to the query term from the search results, and makes the user can not recognize the vertical search types having the highest relevance to the respective recommended query terms. Meanwhile, in order to select the final vertical search results, the user often has to perform a second click on the link of the relevant vertical search channel, resulting in an increase in the member of human-machine interactions on the Internet machine side, and each human-machine interaction, in turn, sends operation request information, triggers a computation process and generates responding result information, and accordingly more resources on the machine side, including client resources, server resources, network bandwidth resource, etc., will be occupied.